Table of Contents
- Credits L-T-P [C]: 3-0-3 [4.5]
- Expectation from 4000 level course:
- 1 Contact Hr + 2 Non-Contact Hr
- Learn by Assignments/Experiments
- Where: LHB 307
- Slot: F (Monday, Tuesday, and Thursday 2:00 PM - 2:50 PM)
- Lab Slot: Will be Announced
- LMS: Moodle
- Credential: Internet ID/Password
- Easy Enrollment Code: ei5nm4
- Easy Enrollment QR:
- Introduction: Data for Graphics, Design principles, Value for visualization, Categorical, time series, and statistical data graphics, Introduction to Visualization Tools
- Graphics Pipeline: Introduction, Primitives: vertices, edges, triangles, Model transforms: translations, rotations, scaling, View transform, Perspective transform, window transform
- Aesthetics and Perception: Graphical Perception Theory, Experimentation, and the Application, Graphical Integrity, Layering and Separation, Color and Information, Using Space Effectively
- Visualization Design: Visual Display of Quantitative Information, Data-Ink Maximization, Graphical Design, Exploratory Data Analysis, Heat Map
- Multidimensional Data: Query, Analysis and Visualization of Multi-dimensional Relational Databases, Interactive Exploration, tSNE
- Interaction: Interactive Dynamics for Visual Analysis, Visual Queries, Finding Patterns in Time Series Data, Trend visualization, Animation, Dashboard, Visual Storytelling
- Collaboration: Graph Visualization and Navigation, Online Social Networks, Social Data Analysis, Collaborative Visual Analytics, Text, Map, Geospatial data
- Visualization Design, Exploratory data analysis, Interactive Visualization Tools like Tableau, Gephi, D3, etc. Mini Project.
- We will be using Python Dash, R-Shiny, D3 JS, Grephi in our lab work
- E. TUFTE (2001), The Visual Display of Quantitative Information, Graphics Press, 2nd Edition.
- J. KOPONEN, J. HILDÉN (2019), Data Visualization Handbook, CRC Press.
- M. LIMA (2014), The Book of Trees: Visualizing Branches of Knowledge, Princeton Architectural Press.
- R. TAMASSIA (2013), Handbook of Graph Drawing and Visualization, CRC Press.
- S. MURRAY (2017), Interactive Data Visualization for the Web, O’Reilly Press, 2nd Edition.
As per the notification from academics 100% attendance is mandatory. If you have genuine reason please take leave approval as per academics rule.
If attendance falls below 75%, one should get at least C grade to pass the course. Otherwise F grade will be assigned.
||10% + 10%
- There will be about 3 - 4 quizzes; best 2 will be considered for grading.
- All the quizzes will be in Moodle Platform.
- No makeup quiz will be taken considering there will be more than required no of quizzes.
- Students need to build visualization project as part of the lab exercise
- Projects can be done in group
- Max 2 member group is allowed
- Students will be asked to work on different graded assignments
- Best five scores will be considered for grading
Quiz dates will be announced during class
Plagiarism tolerance is 7% from single source and 15% cumulative, anything more will reduce your marks as follows:
- Any logical/conceptual/formulation plagiarism: zero marks
- Other form of plagiarism (above 50%): zero marks
- Otherwise: Percentage of plagiarism will be deducted from the obtained mark